Prediction of Gene Function using Phylogenetic Trees
2023-06-05
You can download the slides from https://ggv.cl/image-retreat2023
“Bayesian Parameter Estimation for Automatic Annotation of Gene Functions Using Observational Data and Phylogenetic Trees” – (G. G. Vega Yon et al. 2021)
using phylogenetic trees to predict gene function.
The key difference between the models is how they model the transition from parent to offspring: \(\mathbb{P}\left(x_n\to x_o\right)\)
A key part of molecular innovation, gene duplication provides an opportunity for new functions to emerge (wikimedia)
Model fully implemented in C++ and R. It already shows great promise:
Sub Aim 1 is supposed to deal with the Hierarchical Bayesian Framework.
Some work is done, but we need a leader for this.
Most trees aren’t fully-reconciled: GEESE’s complexity grows exponentially with the number of offspring in the tree.
Negative assertions (gene NOT associated with) are rare… but Christopher has made good progress using taxon constraints.
The GEESE paper is about 80% done. We need towill finish it this year.
Low-hanging fruit: aphylo2 can be submitted to PLOS Comp. Bio. using aphylo (a software prototype is up and running).
Not mentioned in the original grant, but we could add it to (any) the project.
George G. Vega Yon – ggv.cl/slides/image-retreat2023